{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load the JSON file which describes the human pose task.  This is in COCO format, it is the category descriptor pulled from the annotations file.  We modify the COCO category slightly, to add a neck keypoint.  We will use this task description JSON to create a topology tensor, which is an intermediate data structure that describes the part linkages, as well as which channels in the part affinity field each linkage corresponds to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import trt_pose.coco\n",
    "\n",
    "with open('human_pose.json', 'r') as f:\n",
    "    human_pose = json.load(f)\n",
    "\n",
    "topology = trt_pose.coco.coco_category_to_topology(human_pose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll load our model.  Each model takes at least two parameters, *cmap_channels* and *paf_channels* corresponding to the number of heatmap channels\n",
    "and part affinity field channels.  The number of part affinity field channels is 2x the number of links, because each link has a channel corresponding to the\n",
    "x and y direction of the vector field for each link."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import trt_pose.models\n",
    "\n",
    "num_parts = len(human_pose['keypoints'])\n",
    "num_links = len(human_pose['skeleton'])\n",
    "\n",
    "# model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links, pretrained=False).cuda().eval()\n",
    "model = trt_pose.models.densenet121_baseline_att(num_parts, 2 * num_links, pretrained=False).cuda().eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load the model weights.  You will need to download these according to the table in the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IncompatibleKeys(missing_keys=[], unexpected_keys=[])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# MODEL_WEIGHTS = 'resnet18_baseline_att_224x224_A_epoch_249.pth'\n",
    "# ONNX_OUTPUT = 'resnet18_baseline_att_224x224_A_epoch_249.onnx'\n",
    "MODEL_WEIGHTS = 'densenet121_baseline_att_256x256_B_epoch_160.pth'\n",
    "ONNX_OUTPUT = 'densenet121_baseline_att_256x256_B_epoch_160.onnx'\n",
    "\n",
    "model.load_state_dict(torch.load(MODEL_WEIGHTS))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert a pytorch model to ONNX format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WIDTH = 224\n",
    "# HEIGHT = 224\n",
    "WIDTH = 256\n",
    "HEIGHT = 256\n",
    "\n",
    "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n",
    "\n",
    "torch_out = model(data)\n",
    "torch.onnx.export(model,\n",
    "                  data,\n",
    "                  ONNX_OUTPUT,\n",
    "                  export_params=True,\n",
    "                  opset_version=10,\n",
    "                  do_constant_folding=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify converted ONNX model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph torch-jit-export (\n",
      "  %0[FLOAT, 1x3x256x256]\n",
      ") optional inputs with matching initializers (\n",
      "  %0.densenet.features.conv0.weight[FLOAT, 64x3x7x7]\n",
      "  %0.densenet.features.norm0.weight[FLOAT, 64]\n",
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      "  %0.densenet.features.denseblock1.denselayer1.norm1.weight[FLOAT, 64]\n",
      "  %0.densenet.features.denseblock1.denselayer1.norm1.bias[FLOAT, 64]\n",
      "  %0.densenet.features.denseblock1.denselayer1.norm1.running_mean[FLOAT, 64]\n",
      "  %0.densenet.features.denseblock1.denselayer1.norm1.running_var[FLOAT, 64]\n",
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      "  %0.densenet.features.denseblock1.denselayer1.conv1.weight[FLOAT, 128x64x1x1]\n",
      "  %0.densenet.features.denseblock1.denselayer1.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock1.denselayer1.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock1.denselayer1.norm2.running_mean[FLOAT, 128]\n",
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      "  %0.densenet.features.denseblock1.denselayer1.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock1.denselayer1.conv2.weight[FLOAT, 32x128x3x3]\n",
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      "  %0.densenet.features.denseblock4.denselayer9.norm1.weight[FLOAT, 768]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm1.bias[FLOAT, 768]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm1.running_mean[FLOAT, 768]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm1.running_var[FLOAT, 768]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm1.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer9.conv1.weight[FLOAT, 128x768x1x1]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm2.running_mean[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm2.running_var[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer9.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer9.conv2.weight[FLOAT, 32x128x3x3]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm1.weight[FLOAT, 800]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm1.bias[FLOAT, 800]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm1.running_mean[FLOAT, 800]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm1.running_var[FLOAT, 800]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm1.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer10.conv1.weight[FLOAT, 128x800x1x1]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm2.running_mean[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm2.running_var[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer10.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer10.conv2.weight[FLOAT, 32x128x3x3]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm1.weight[FLOAT, 832]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm1.bias[FLOAT, 832]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm1.running_mean[FLOAT, 832]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm1.running_var[FLOAT, 832]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm1.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer11.conv1.weight[FLOAT, 128x832x1x1]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm2.running_mean[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm2.running_var[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer11.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer11.conv2.weight[FLOAT, 32x128x3x3]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm1.weight[FLOAT, 864]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm1.bias[FLOAT, 864]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm1.running_mean[FLOAT, 864]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm1.running_var[FLOAT, 864]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm1.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer12.conv1.weight[FLOAT, 128x864x1x1]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm2.running_mean[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm2.running_var[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer12.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer12.conv2.weight[FLOAT, 32x128x3x3]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm1.weight[FLOAT, 896]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm1.bias[FLOAT, 896]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm1.running_mean[FLOAT, 896]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm1.running_var[FLOAT, 896]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm1.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer13.conv1.weight[FLOAT, 128x896x1x1]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm2.running_mean[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm2.running_var[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer13.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer13.conv2.weight[FLOAT, 32x128x3x3]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm1.weight[FLOAT, 928]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm1.bias[FLOAT, 928]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm1.running_mean[FLOAT, 928]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm1.running_var[FLOAT, 928]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm1.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer14.conv1.weight[FLOAT, 128x928x1x1]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm2.running_mean[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm2.running_var[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer14.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer14.conv2.weight[FLOAT, 32x128x3x3]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm1.weight[FLOAT, 960]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm1.bias[FLOAT, 960]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm1.running_mean[FLOAT, 960]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm1.running_var[FLOAT, 960]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm1.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer15.conv1.weight[FLOAT, 128x960x1x1]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm2.running_mean[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm2.running_var[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer15.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer15.conv2.weight[FLOAT, 32x128x3x3]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm1.weight[FLOAT, 992]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm1.bias[FLOAT, 992]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm1.running_mean[FLOAT, 992]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm1.running_var[FLOAT, 992]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm1.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer16.conv1.weight[FLOAT, 128x992x1x1]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm2.weight[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm2.bias[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm2.running_mean[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm2.running_var[FLOAT, 128]\n",
      "  %0.densenet.features.denseblock4.denselayer16.norm2.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.features.denseblock4.denselayer16.conv2.weight[FLOAT, 32x128x3x3]\n",
      "  %0.densenet.features.norm5.weight[FLOAT, 1024]\n",
      "  %0.densenet.features.norm5.bias[FLOAT, 1024]\n",
      "  %0.densenet.features.norm5.running_mean[FLOAT, 1024]\n",
      "  %0.densenet.features.norm5.running_var[FLOAT, 1024]\n",
      "  %0.densenet.features.norm5.num_batches_tracked[INT64, scalar]\n",
      "  %0.densenet.classifier.weight[FLOAT, 1000x1024]\n",
      "  %0.densenet.classifier.bias[FLOAT, 1000]\n",
      "  %1.cmap_up.0.weight[FLOAT, 1024x256x4x4]\n",
      "  %1.cmap_up.0.bias[FLOAT, 256]\n",
      "  %1.cmap_up.1.weight[FLOAT, 256]\n",
      "  %1.cmap_up.1.bias[FLOAT, 256]\n",
      "  %1.cmap_up.1.running_mean[FLOAT, 256]\n",
      "  %1.cmap_up.1.running_var[FLOAT, 256]\n",
      "  %1.cmap_up.1.num_batches_tracked[INT64, scalar]\n",
      "  %1.cmap_up.3.weight[FLOAT, 256x256x4x4]\n",
      "  %1.cmap_up.3.bias[FLOAT, 256]\n",
      "  %1.cmap_up.4.weight[FLOAT, 256]\n",
      "  %1.cmap_up.4.bias[FLOAT, 256]\n",
      "  %1.cmap_up.4.running_mean[FLOAT, 256]\n",
      "  %1.cmap_up.4.running_var[FLOAT, 256]\n",
      "  %1.cmap_up.4.num_batches_tracked[INT64, scalar]\n",
      "  %1.cmap_up.6.weight[FLOAT, 256x256x4x4]\n",
      "  %1.cmap_up.6.bias[FLOAT, 256]\n",
      "  %1.cmap_up.7.weight[FLOAT, 256]\n",
      "  %1.cmap_up.7.bias[FLOAT, 256]\n",
      "  %1.cmap_up.7.running_mean[FLOAT, 256]\n",
      "  %1.cmap_up.7.running_var[FLOAT, 256]\n",
      "  %1.cmap_up.7.num_batches_tracked[INT64, scalar]\n",
      "  %1.paf_up.0.weight[FLOAT, 1024x256x4x4]\n",
      "  %1.paf_up.0.bias[FLOAT, 256]\n",
      "  %1.paf_up.1.weight[FLOAT, 256]\n",
      "  %1.paf_up.1.bias[FLOAT, 256]\n",
      "  %1.paf_up.1.running_mean[FLOAT, 256]\n",
      "  %1.paf_up.1.running_var[FLOAT, 256]\n",
      "  %1.paf_up.1.num_batches_tracked[INT64, scalar]\n",
      "  %1.paf_up.3.weight[FLOAT, 256x256x4x4]\n",
      "  %1.paf_up.3.bias[FLOAT, 256]\n",
      "  %1.paf_up.4.weight[FLOAT, 256]\n",
      "  %1.paf_up.4.bias[FLOAT, 256]\n",
      "  %1.paf_up.4.running_mean[FLOAT, 256]\n",
      "  %1.paf_up.4.running_var[FLOAT, 256]\n",
      "  %1.paf_up.4.num_batches_tracked[INT64, scalar]\n",
      "  %1.paf_up.6.weight[FLOAT, 256x256x4x4]\n",
      "  %1.paf_up.6.bias[FLOAT, 256]\n",
      "  %1.paf_up.7.weight[FLOAT, 256]\n",
      "  %1.paf_up.7.bias[FLOAT, 256]\n",
      "  %1.paf_up.7.running_mean[FLOAT, 256]\n",
      "  %1.paf_up.7.running_var[FLOAT, 256]\n",
      "  %1.paf_up.7.num_batches_tracked[INT64, scalar]\n",
      "  %1.cmap_att.weight[FLOAT, 256x256x3x3]\n",
      "  %1.cmap_att.bias[FLOAT, 256]\n",
      "  %1.paf_att.weight[FLOAT, 256x256x3x3]\n",
      "  %1.paf_att.bias[FLOAT, 256]\n",
      "  %1.cmap_conv.weight[FLOAT, 18x256x1x1]\n",
      "  %1.cmap_conv.bias[FLOAT, 18]\n",
      "  %1.paf_conv.weight[FLOAT, 42x256x1x1]\n",
      "  %1.paf_conv.bias[FLOAT, 42]\n",
      ") {\n",
      "  %778 = Conv[dilations = [1, 1], group = 1, kernel_shape = [7, 7], pads = [3, 3, 3, 3], strides = [2, 2]](%0, %0.densenet.features.conv0.weight)\n",
      "  %779 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%778, %0.densenet.features.norm0.weight, %0.densenet.features.norm0.bias, %0.densenet.features.norm0.running_mean, %0.densenet.features.norm0.running_var)\n",
      "  %780 = Relu(%779)\n",
      "  %781 = MaxPool[kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%780)\n",
      "  %782 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%781, %0.densenet.features.denseblock1.denselayer1.norm1.weight, %0.densenet.features.denseblock1.denselayer1.norm1.bias, %0.densenet.features.denseblock1.denselayer1.norm1.running_mean, %0.densenet.features.denseblock1.denselayer1.norm1.running_var)\n",
      "  %783 = Relu(%782)\n",
      "  %784 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%783, %0.densenet.features.denseblock1.denselayer1.conv1.weight)\n",
      "  %785 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%784, %0.densenet.features.denseblock1.denselayer1.norm2.weight, %0.densenet.features.denseblock1.denselayer1.norm2.bias, %0.densenet.features.denseblock1.denselayer1.norm2.running_mean, %0.densenet.features.denseblock1.denselayer1.norm2.running_var)\n",
      "  %786 = Relu(%785)\n",
      "  %787 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%786, %0.densenet.features.denseblock1.denselayer1.conv2.weight)\n",
      "  %788 = Concat[axis = 1](%781, %787)\n",
      "  %789 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%788, %0.densenet.features.denseblock1.denselayer2.norm1.weight, %0.densenet.features.denseblock1.denselayer2.norm1.bias, %0.densenet.features.denseblock1.denselayer2.norm1.running_mean, %0.densenet.features.denseblock1.denselayer2.norm1.running_var)\n",
      "  %790 = Relu(%789)\n",
      "  %791 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%790, %0.densenet.features.denseblock1.denselayer2.conv1.weight)\n",
      "  %792 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%791, %0.densenet.features.denseblock1.denselayer2.norm2.weight, %0.densenet.features.denseblock1.denselayer2.norm2.bias, %0.densenet.features.denseblock1.denselayer2.norm2.running_mean, %0.densenet.features.denseblock1.denselayer2.norm2.running_var)\n",
      "  %793 = Relu(%792)\n",
      "  %794 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%793, %0.densenet.features.denseblock1.denselayer2.conv2.weight)\n",
      "  %795 = Concat[axis = 1](%788, %794)\n",
      "  %796 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%795, %0.densenet.features.denseblock1.denselayer3.norm1.weight, %0.densenet.features.denseblock1.denselayer3.norm1.bias, %0.densenet.features.denseblock1.denselayer3.norm1.running_mean, %0.densenet.features.denseblock1.denselayer3.norm1.running_var)\n",
      "  %797 = Relu(%796)\n",
      "  %798 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%797, %0.densenet.features.denseblock1.denselayer3.conv1.weight)\n",
      "  %799 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%798, %0.densenet.features.denseblock1.denselayer3.norm2.weight, %0.densenet.features.denseblock1.denselayer3.norm2.bias, %0.densenet.features.denseblock1.denselayer3.norm2.running_mean, %0.densenet.features.denseblock1.denselayer3.norm2.running_var)\n",
      "  %800 = Relu(%799)\n",
      "  %801 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%800, %0.densenet.features.denseblock1.denselayer3.conv2.weight)\n",
      "  %802 = Concat[axis = 1](%795, %801)\n",
      "  %803 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%802, %0.densenet.features.denseblock1.denselayer4.norm1.weight, %0.densenet.features.denseblock1.denselayer4.norm1.bias, %0.densenet.features.denseblock1.denselayer4.norm1.running_mean, %0.densenet.features.denseblock1.denselayer4.norm1.running_var)\n",
      "  %804 = Relu(%803)\n",
      "  %805 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%804, %0.densenet.features.denseblock1.denselayer4.conv1.weight)\n",
      "  %806 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%805, %0.densenet.features.denseblock1.denselayer4.norm2.weight, %0.densenet.features.denseblock1.denselayer4.norm2.bias, %0.densenet.features.denseblock1.denselayer4.norm2.running_mean, %0.densenet.features.denseblock1.denselayer4.norm2.running_var)\n",
      "  %807 = Relu(%806)\n",
      "  %808 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%807, %0.densenet.features.denseblock1.denselayer4.conv2.weight)\n",
      "  %809 = Concat[axis = 1](%802, %808)\n",
      "  %810 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%809, %0.densenet.features.denseblock1.denselayer5.norm1.weight, %0.densenet.features.denseblock1.denselayer5.norm1.bias, %0.densenet.features.denseblock1.denselayer5.norm1.running_mean, %0.densenet.features.denseblock1.denselayer5.norm1.running_var)\n",
      "  %811 = Relu(%810)\n",
      "  %812 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%811, %0.densenet.features.denseblock1.denselayer5.conv1.weight)\n",
      "  %813 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%812, %0.densenet.features.denseblock1.denselayer5.norm2.weight, %0.densenet.features.denseblock1.denselayer5.norm2.bias, %0.densenet.features.denseblock1.denselayer5.norm2.running_mean, %0.densenet.features.denseblock1.denselayer5.norm2.running_var)\n",
      "  %814 = Relu(%813)\n",
      "  %815 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%814, %0.densenet.features.denseblock1.denselayer5.conv2.weight)\n",
      "  %816 = Concat[axis = 1](%809, %815)\n",
      "  %817 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%816, %0.densenet.features.denseblock1.denselayer6.norm1.weight, %0.densenet.features.denseblock1.denselayer6.norm1.bias, %0.densenet.features.denseblock1.denselayer6.norm1.running_mean, %0.densenet.features.denseblock1.denselayer6.norm1.running_var)\n",
      "  %818 = Relu(%817)\n",
      "  %819 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%818, %0.densenet.features.denseblock1.denselayer6.conv1.weight)\n",
      "  %820 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%819, %0.densenet.features.denseblock1.denselayer6.norm2.weight, %0.densenet.features.denseblock1.denselayer6.norm2.bias, %0.densenet.features.denseblock1.denselayer6.norm2.running_mean, %0.densenet.features.denseblock1.denselayer6.norm2.running_var)\n",
      "  %821 = Relu(%820)\n",
      "  %822 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%821, %0.densenet.features.denseblock1.denselayer6.conv2.weight)\n",
      "  %823 = Concat[axis = 1](%816, %822)\n",
      "  %824 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%823, %0.densenet.features.transition1.norm.weight, %0.densenet.features.transition1.norm.bias, %0.densenet.features.transition1.norm.running_mean, %0.densenet.features.transition1.norm.running_var)\n",
      "  %825 = Relu(%824)\n",
      "  %826 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%825, %0.densenet.features.transition1.conv.weight)\n",
      "  %827 = Pad[mode = 'constant', pads = [0, 0, 0, 0, 0, 0, 0, 0], value = 0](%826)\n",
      "  %828 = AveragePool[kernel_shape = [2, 2], pads = [0, 0, 0, 0], strides = [2, 2]](%827)\n",
      "  %829 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%828, %0.densenet.features.denseblock2.denselayer1.norm1.weight, %0.densenet.features.denseblock2.denselayer1.norm1.bias, %0.densenet.features.denseblock2.denselayer1.norm1.running_mean, %0.densenet.features.denseblock2.denselayer1.norm1.running_var)\n",
      "  %830 = Relu(%829)\n",
      "  %831 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%830, %0.densenet.features.denseblock2.denselayer1.conv1.weight)\n",
      "  %832 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%831, %0.densenet.features.denseblock2.denselayer1.norm2.weight, %0.densenet.features.denseblock2.denselayer1.norm2.bias, %0.densenet.features.denseblock2.denselayer1.norm2.running_mean, %0.densenet.features.denseblock2.denselayer1.norm2.running_var)\n",
      "  %833 = Relu(%832)\n",
      "  %834 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%833, %0.densenet.features.denseblock2.denselayer1.conv2.weight)\n",
      "  %835 = Concat[axis = 1](%828, %834)\n",
      "  %836 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%835, %0.densenet.features.denseblock2.denselayer2.norm1.weight, %0.densenet.features.denseblock2.denselayer2.norm1.bias, %0.densenet.features.denseblock2.denselayer2.norm1.running_mean, %0.densenet.features.denseblock2.denselayer2.norm1.running_var)\n",
      "  %837 = Relu(%836)\n",
      "  %838 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%837, %0.densenet.features.denseblock2.denselayer2.conv1.weight)\n",
      "  %839 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%838, %0.densenet.features.denseblock2.denselayer2.norm2.weight, %0.densenet.features.denseblock2.denselayer2.norm2.bias, %0.densenet.features.denseblock2.denselayer2.norm2.running_mean, %0.densenet.features.denseblock2.denselayer2.norm2.running_var)\n",
      "  %840 = Relu(%839)\n",
      "  %841 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%840, %0.densenet.features.denseblock2.denselayer2.conv2.weight)\n",
      "  %842 = Concat[axis = 1](%835, %841)\n",
      "  %843 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%842, %0.densenet.features.denseblock2.denselayer3.norm1.weight, %0.densenet.features.denseblock2.denselayer3.norm1.bias, %0.densenet.features.denseblock2.denselayer3.norm1.running_mean, %0.densenet.features.denseblock2.denselayer3.norm1.running_var)\n",
      "  %844 = Relu(%843)\n",
      "  %845 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%844, %0.densenet.features.denseblock2.denselayer3.conv1.weight)\n",
      "  %846 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%845, %0.densenet.features.denseblock2.denselayer3.norm2.weight, %0.densenet.features.denseblock2.denselayer3.norm2.bias, %0.densenet.features.denseblock2.denselayer3.norm2.running_mean, %0.densenet.features.denseblock2.denselayer3.norm2.running_var)\n",
      "  %847 = Relu(%846)\n",
      "  %848 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%847, %0.densenet.features.denseblock2.denselayer3.conv2.weight)\n",
      "  %849 = Concat[axis = 1](%842, %848)\n",
      "  %850 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%849, %0.densenet.features.denseblock2.denselayer4.norm1.weight, %0.densenet.features.denseblock2.denselayer4.norm1.bias, %0.densenet.features.denseblock2.denselayer4.norm1.running_mean, %0.densenet.features.denseblock2.denselayer4.norm1.running_var)\n",
      "  %851 = Relu(%850)\n",
      "  %852 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%851, %0.densenet.features.denseblock2.denselayer4.conv1.weight)\n",
      "  %853 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%852, %0.densenet.features.denseblock2.denselayer4.norm2.weight, %0.densenet.features.denseblock2.denselayer4.norm2.bias, %0.densenet.features.denseblock2.denselayer4.norm2.running_mean, %0.densenet.features.denseblock2.denselayer4.norm2.running_var)\n",
      "  %854 = Relu(%853)\n",
      "  %855 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%854, %0.densenet.features.denseblock2.denselayer4.conv2.weight)\n",
      "  %856 = Concat[axis = 1](%849, %855)\n",
      "  %857 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%856, %0.densenet.features.denseblock2.denselayer5.norm1.weight, %0.densenet.features.denseblock2.denselayer5.norm1.bias, %0.densenet.features.denseblock2.denselayer5.norm1.running_mean, %0.densenet.features.denseblock2.denselayer5.norm1.running_var)\n",
      "  %858 = Relu(%857)\n",
      "  %859 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%858, %0.densenet.features.denseblock2.denselayer5.conv1.weight)\n",
      "  %860 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%859, %0.densenet.features.denseblock2.denselayer5.norm2.weight, %0.densenet.features.denseblock2.denselayer5.norm2.bias, %0.densenet.features.denseblock2.denselayer5.norm2.running_mean, %0.densenet.features.denseblock2.denselayer5.norm2.running_var)\n",
      "  %861 = Relu(%860)\n",
      "  %862 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%861, %0.densenet.features.denseblock2.denselayer5.conv2.weight)\n",
      "  %863 = Concat[axis = 1](%856, %862)\n",
      "  %864 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%863, %0.densenet.features.denseblock2.denselayer6.norm1.weight, %0.densenet.features.denseblock2.denselayer6.norm1.bias, %0.densenet.features.denseblock2.denselayer6.norm1.running_mean, %0.densenet.features.denseblock2.denselayer6.norm1.running_var)\n",
      "  %865 = Relu(%864)\n",
      "  %866 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%865, %0.densenet.features.denseblock2.denselayer6.conv1.weight)\n",
      "  %867 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%866, %0.densenet.features.denseblock2.denselayer6.norm2.weight, %0.densenet.features.denseblock2.denselayer6.norm2.bias, %0.densenet.features.denseblock2.denselayer6.norm2.running_mean, %0.densenet.features.denseblock2.denselayer6.norm2.running_var)\n",
      "  %868 = Relu(%867)\n",
      "  %869 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%868, %0.densenet.features.denseblock2.denselayer6.conv2.weight)\n",
      "  %870 = Concat[axis = 1](%863, %869)\n",
      "  %871 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%870, %0.densenet.features.denseblock2.denselayer7.norm1.weight, %0.densenet.features.denseblock2.denselayer7.norm1.bias, %0.densenet.features.denseblock2.denselayer7.norm1.running_mean, %0.densenet.features.denseblock2.denselayer7.norm1.running_var)\n",
      "  %872 = Relu(%871)\n",
      "  %873 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%872, %0.densenet.features.denseblock2.denselayer7.conv1.weight)\n",
      "  %874 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%873, %0.densenet.features.denseblock2.denselayer7.norm2.weight, %0.densenet.features.denseblock2.denselayer7.norm2.bias, %0.densenet.features.denseblock2.denselayer7.norm2.running_mean, %0.densenet.features.denseblock2.denselayer7.norm2.running_var)\n",
      "  %875 = Relu(%874)\n",
      "  %876 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%875, %0.densenet.features.denseblock2.denselayer7.conv2.weight)\n",
      "  %877 = Concat[axis = 1](%870, %876)\n",
      "  %878 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%877, %0.densenet.features.denseblock2.denselayer8.norm1.weight, %0.densenet.features.denseblock2.denselayer8.norm1.bias, %0.densenet.features.denseblock2.denselayer8.norm1.running_mean, %0.densenet.features.denseblock2.denselayer8.norm1.running_var)\n",
      "  %879 = Relu(%878)\n",
      "  %880 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%879, %0.densenet.features.denseblock2.denselayer8.conv1.weight)\n",
      "  %881 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%880, %0.densenet.features.denseblock2.denselayer8.norm2.weight, %0.densenet.features.denseblock2.denselayer8.norm2.bias, %0.densenet.features.denseblock2.denselayer8.norm2.running_mean, %0.densenet.features.denseblock2.denselayer8.norm2.running_var)\n",
      "  %882 = Relu(%881)\n",
      "  %883 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%882, %0.densenet.features.denseblock2.denselayer8.conv2.weight)\n",
      "  %884 = Concat[axis = 1](%877, %883)\n",
      "  %885 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%884, %0.densenet.features.denseblock2.denselayer9.norm1.weight, %0.densenet.features.denseblock2.denselayer9.norm1.bias, %0.densenet.features.denseblock2.denselayer9.norm1.running_mean, %0.densenet.features.denseblock2.denselayer9.norm1.running_var)\n",
      "  %886 = Relu(%885)\n",
      "  %887 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%886, %0.densenet.features.denseblock2.denselayer9.conv1.weight)\n",
      "  %888 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%887, %0.densenet.features.denseblock2.denselayer9.norm2.weight, %0.densenet.features.denseblock2.denselayer9.norm2.bias, %0.densenet.features.denseblock2.denselayer9.norm2.running_mean, %0.densenet.features.denseblock2.denselayer9.norm2.running_var)\n",
      "  %889 = Relu(%888)\n",
      "  %890 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%889, %0.densenet.features.denseblock2.denselayer9.conv2.weight)\n",
      "  %891 = Concat[axis = 1](%884, %890)\n",
      "  %892 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%891, %0.densenet.features.denseblock2.denselayer10.norm1.weight, %0.densenet.features.denseblock2.denselayer10.norm1.bias, %0.densenet.features.denseblock2.denselayer10.norm1.running_mean, %0.densenet.features.denseblock2.denselayer10.norm1.running_var)\n",
      "  %893 = Relu(%892)\n",
      "  %894 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%893, %0.densenet.features.denseblock2.denselayer10.conv1.weight)\n",
      "  %895 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%894, %0.densenet.features.denseblock2.denselayer10.norm2.weight, %0.densenet.features.denseblock2.denselayer10.norm2.bias, %0.densenet.features.denseblock2.denselayer10.norm2.running_mean, %0.densenet.features.denseblock2.denselayer10.norm2.running_var)\n",
      "  %896 = Relu(%895)\n",
      "  %897 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%896, %0.densenet.features.denseblock2.denselayer10.conv2.weight)\n",
      "  %898 = Concat[axis = 1](%891, %897)\n",
      "  %899 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%898, %0.densenet.features.denseblock2.denselayer11.norm1.weight, %0.densenet.features.denseblock2.denselayer11.norm1.bias, %0.densenet.features.denseblock2.denselayer11.norm1.running_mean, %0.densenet.features.denseblock2.denselayer11.norm1.running_var)\n",
      "  %900 = Relu(%899)\n",
      "  %901 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%900, %0.densenet.features.denseblock2.denselayer11.conv1.weight)\n",
      "  %902 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%901, %0.densenet.features.denseblock2.denselayer11.norm2.weight, %0.densenet.features.denseblock2.denselayer11.norm2.bias, %0.densenet.features.denseblock2.denselayer11.norm2.running_mean, %0.densenet.features.denseblock2.denselayer11.norm2.running_var)\n",
      "  %903 = Relu(%902)\n",
      "  %904 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%903, %0.densenet.features.denseblock2.denselayer11.conv2.weight)\n",
      "  %905 = Concat[axis = 1](%898, %904)\n",
      "  %906 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%905, %0.densenet.features.denseblock2.denselayer12.norm1.weight, %0.densenet.features.denseblock2.denselayer12.norm1.bias, %0.densenet.features.denseblock2.denselayer12.norm1.running_mean, %0.densenet.features.denseblock2.denselayer12.norm1.running_var)\n",
      "  %907 = Relu(%906)\n",
      "  %908 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%907, %0.densenet.features.denseblock2.denselayer12.conv1.weight)\n",
      "  %909 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%908, %0.densenet.features.denseblock2.denselayer12.norm2.weight, %0.densenet.features.denseblock2.denselayer12.norm2.bias, %0.densenet.features.denseblock2.denselayer12.norm2.running_mean, %0.densenet.features.denseblock2.denselayer12.norm2.running_var)\n",
      "  %910 = Relu(%909)\n",
      "  %911 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%910, %0.densenet.features.denseblock2.denselayer12.conv2.weight)\n",
      "  %912 = Concat[axis = 1](%905, %911)\n",
      "  %913 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%912, %0.densenet.features.transition2.norm.weight, %0.densenet.features.transition2.norm.bias, %0.densenet.features.transition2.norm.running_mean, %0.densenet.features.transition2.norm.running_var)\n",
      "  %914 = Relu(%913)\n",
      "  %915 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%914, %0.densenet.features.transition2.conv.weight)\n",
      "  %916 = Pad[mode = 'constant', pads = [0, 0, 0, 0, 0, 0, 0, 0], value = 0](%915)\n",
      "  %917 = AveragePool[kernel_shape = [2, 2], pads = [0, 0, 0, 0], strides = [2, 2]](%916)\n",
      "  %918 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%917, %0.densenet.features.denseblock3.denselayer1.norm1.weight, %0.densenet.features.denseblock3.denselayer1.norm1.bias, %0.densenet.features.denseblock3.denselayer1.norm1.running_mean, %0.densenet.features.denseblock3.denselayer1.norm1.running_var)\n",
      "  %919 = Relu(%918)\n",
      "  %920 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%919, %0.densenet.features.denseblock3.denselayer1.conv1.weight)\n",
      "  %921 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%920, %0.densenet.features.denseblock3.denselayer1.norm2.weight, %0.densenet.features.denseblock3.denselayer1.norm2.bias, %0.densenet.features.denseblock3.denselayer1.norm2.running_mean, %0.densenet.features.denseblock3.denselayer1.norm2.running_var)\n",
      "  %922 = Relu(%921)\n",
      "  %923 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%922, %0.densenet.features.denseblock3.denselayer1.conv2.weight)\n",
      "  %924 = Concat[axis = 1](%917, %923)\n",
      "  %925 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%924, %0.densenet.features.denseblock3.denselayer2.norm1.weight, %0.densenet.features.denseblock3.denselayer2.norm1.bias, %0.densenet.features.denseblock3.denselayer2.norm1.running_mean, %0.densenet.features.denseblock3.denselayer2.norm1.running_var)\n",
      "  %926 = Relu(%925)\n",
      "  %927 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%926, %0.densenet.features.denseblock3.denselayer2.conv1.weight)\n",
      "  %928 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%927, %0.densenet.features.denseblock3.denselayer2.norm2.weight, %0.densenet.features.denseblock3.denselayer2.norm2.bias, %0.densenet.features.denseblock3.denselayer2.norm2.running_mean, %0.densenet.features.denseblock3.denselayer2.norm2.running_var)\n",
      "  %929 = Relu(%928)\n",
      "  %930 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%929, %0.densenet.features.denseblock3.denselayer2.conv2.weight)\n",
      "  %931 = Concat[axis = 1](%924, %930)\n",
      "  %932 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%931, %0.densenet.features.denseblock3.denselayer3.norm1.weight, %0.densenet.features.denseblock3.denselayer3.norm1.bias, %0.densenet.features.denseblock3.denselayer3.norm1.running_mean, %0.densenet.features.denseblock3.denselayer3.norm1.running_var)\n",
      "  %933 = Relu(%932)\n",
      "  %934 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%933, %0.densenet.features.denseblock3.denselayer3.conv1.weight)\n",
      "  %935 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%934, %0.densenet.features.denseblock3.denselayer3.norm2.weight, %0.densenet.features.denseblock3.denselayer3.norm2.bias, %0.densenet.features.denseblock3.denselayer3.norm2.running_mean, %0.densenet.features.denseblock3.denselayer3.norm2.running_var)\n",
      "  %936 = Relu(%935)\n",
      "  %937 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%936, %0.densenet.features.denseblock3.denselayer3.conv2.weight)\n",
      "  %938 = Concat[axis = 1](%931, %937)\n",
      "  %939 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%938, %0.densenet.features.denseblock3.denselayer4.norm1.weight, %0.densenet.features.denseblock3.denselayer4.norm1.bias, %0.densenet.features.denseblock3.denselayer4.norm1.running_mean, %0.densenet.features.denseblock3.denselayer4.norm1.running_var)\n",
      "  %940 = Relu(%939)\n",
      "  %941 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%940, %0.densenet.features.denseblock3.denselayer4.conv1.weight)\n",
      "  %942 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%941, %0.densenet.features.denseblock3.denselayer4.norm2.weight, %0.densenet.features.denseblock3.denselayer4.norm2.bias, %0.densenet.features.denseblock3.denselayer4.norm2.running_mean, %0.densenet.features.denseblock3.denselayer4.norm2.running_var)\n",
      "  %943 = Relu(%942)\n",
      "  %944 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%943, %0.densenet.features.denseblock3.denselayer4.conv2.weight)\n",
      "  %945 = Concat[axis = 1](%938, %944)\n",
      "  %946 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%945, %0.densenet.features.denseblock3.denselayer5.norm1.weight, %0.densenet.features.denseblock3.denselayer5.norm1.bias, %0.densenet.features.denseblock3.denselayer5.norm1.running_mean, %0.densenet.features.denseblock3.denselayer5.norm1.running_var)\n",
      "  %947 = Relu(%946)\n",
      "  %948 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%947, %0.densenet.features.denseblock3.denselayer5.conv1.weight)\n",
      "  %949 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%948, %0.densenet.features.denseblock3.denselayer5.norm2.weight, %0.densenet.features.denseblock3.denselayer5.norm2.bias, %0.densenet.features.denseblock3.denselayer5.norm2.running_mean, %0.densenet.features.denseblock3.denselayer5.norm2.running_var)\n",
      "  %950 = Relu(%949)\n",
      "  %951 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%950, %0.densenet.features.denseblock3.denselayer5.conv2.weight)\n",
      "  %952 = Concat[axis = 1](%945, %951)\n",
      "  %953 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%952, %0.densenet.features.denseblock3.denselayer6.norm1.weight, %0.densenet.features.denseblock3.denselayer6.norm1.bias, %0.densenet.features.denseblock3.denselayer6.norm1.running_mean, %0.densenet.features.denseblock3.denselayer6.norm1.running_var)\n",
      "  %954 = Relu(%953)\n",
      "  %955 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%954, %0.densenet.features.denseblock3.denselayer6.conv1.weight)\n",
      "  %956 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%955, %0.densenet.features.denseblock3.denselayer6.norm2.weight, %0.densenet.features.denseblock3.denselayer6.norm2.bias, %0.densenet.features.denseblock3.denselayer6.norm2.running_mean, %0.densenet.features.denseblock3.denselayer6.norm2.running_var)\n",
      "  %957 = Relu(%956)\n",
      "  %958 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%957, %0.densenet.features.denseblock3.denselayer6.conv2.weight)\n",
      "  %959 = Concat[axis = 1](%952, %958)\n",
      "  %960 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%959, %0.densenet.features.denseblock3.denselayer7.norm1.weight, %0.densenet.features.denseblock3.denselayer7.norm1.bias, %0.densenet.features.denseblock3.denselayer7.norm1.running_mean, %0.densenet.features.denseblock3.denselayer7.norm1.running_var)\n",
      "  %961 = Relu(%960)\n",
      "  %962 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%961, %0.densenet.features.denseblock3.denselayer7.conv1.weight)\n",
      "  %963 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%962, %0.densenet.features.denseblock3.denselayer7.norm2.weight, %0.densenet.features.denseblock3.denselayer7.norm2.bias, %0.densenet.features.denseblock3.denselayer7.norm2.running_mean, %0.densenet.features.denseblock3.denselayer7.norm2.running_var)\n",
      "  %964 = Relu(%963)\n",
      "  %965 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%964, %0.densenet.features.denseblock3.denselayer7.conv2.weight)\n",
      "  %966 = Concat[axis = 1](%959, %965)\n",
      "  %967 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%966, %0.densenet.features.denseblock3.denselayer8.norm1.weight, %0.densenet.features.denseblock3.denselayer8.norm1.bias, %0.densenet.features.denseblock3.denselayer8.norm1.running_mean, %0.densenet.features.denseblock3.denselayer8.norm1.running_var)\n",
      "  %968 = Relu(%967)\n",
      "  %969 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%968, %0.densenet.features.denseblock3.denselayer8.conv1.weight)\n",
      "  %970 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%969, %0.densenet.features.denseblock3.denselayer8.norm2.weight, %0.densenet.features.denseblock3.denselayer8.norm2.bias, %0.densenet.features.denseblock3.denselayer8.norm2.running_mean, %0.densenet.features.denseblock3.denselayer8.norm2.running_var)\n",
      "  %971 = Relu(%970)\n",
      "  %972 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%971, %0.densenet.features.denseblock3.denselayer8.conv2.weight)\n",
      "  %973 = Concat[axis = 1](%966, %972)\n",
      "  %974 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%973, %0.densenet.features.denseblock3.denselayer9.norm1.weight, %0.densenet.features.denseblock3.denselayer9.norm1.bias, %0.densenet.features.denseblock3.denselayer9.norm1.running_mean, %0.densenet.features.denseblock3.denselayer9.norm1.running_var)\n",
      "  %975 = Relu(%974)\n",
      "  %976 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%975, %0.densenet.features.denseblock3.denselayer9.conv1.weight)\n",
      "  %977 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%976, %0.densenet.features.denseblock3.denselayer9.norm2.weight, %0.densenet.features.denseblock3.denselayer9.norm2.bias, %0.densenet.features.denseblock3.denselayer9.norm2.running_mean, %0.densenet.features.denseblock3.denselayer9.norm2.running_var)\n",
      "  %978 = Relu(%977)\n",
      "  %979 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%978, %0.densenet.features.denseblock3.denselayer9.conv2.weight)\n",
      "  %980 = Concat[axis = 1](%973, %979)\n",
      "  %981 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%980, %0.densenet.features.denseblock3.denselayer10.norm1.weight, %0.densenet.features.denseblock3.denselayer10.norm1.bias, %0.densenet.features.denseblock3.denselayer10.norm1.running_mean, %0.densenet.features.denseblock3.denselayer10.norm1.running_var)\n",
      "  %982 = Relu(%981)\n",
      "  %983 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%982, %0.densenet.features.denseblock3.denselayer10.conv1.weight)\n",
      "  %984 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%983, %0.densenet.features.denseblock3.denselayer10.norm2.weight, %0.densenet.features.denseblock3.denselayer10.norm2.bias, %0.densenet.features.denseblock3.denselayer10.norm2.running_mean, %0.densenet.features.denseblock3.denselayer10.norm2.running_var)\n",
      "  %985 = Relu(%984)\n",
      "  %986 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%985, %0.densenet.features.denseblock3.denselayer10.conv2.weight)\n",
      "  %987 = Concat[axis = 1](%980, %986)\n",
      "  %988 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%987, %0.densenet.features.denseblock3.denselayer11.norm1.weight, %0.densenet.features.denseblock3.denselayer11.norm1.bias, %0.densenet.features.denseblock3.denselayer11.norm1.running_mean, %0.densenet.features.denseblock3.denselayer11.norm1.running_var)\n",
      "  %989 = Relu(%988)\n",
      "  %990 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%989, %0.densenet.features.denseblock3.denselayer11.conv1.weight)\n",
      "  %991 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%990, %0.densenet.features.denseblock3.denselayer11.norm2.weight, %0.densenet.features.denseblock3.denselayer11.norm2.bias, %0.densenet.features.denseblock3.denselayer11.norm2.running_mean, %0.densenet.features.denseblock3.denselayer11.norm2.running_var)\n",
      "  %992 = Relu(%991)\n",
      "  %993 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%992, %0.densenet.features.denseblock3.denselayer11.conv2.weight)\n",
      "  %994 = Concat[axis = 1](%987, %993)\n",
      "  %995 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%994, %0.densenet.features.denseblock3.denselayer12.norm1.weight, %0.densenet.features.denseblock3.denselayer12.norm1.bias, %0.densenet.features.denseblock3.denselayer12.norm1.running_mean, %0.densenet.features.denseblock3.denselayer12.norm1.running_var)\n",
      "  %996 = Relu(%995)\n",
      "  %997 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%996, %0.densenet.features.denseblock3.denselayer12.conv1.weight)\n",
      "  %998 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%997, %0.densenet.features.denseblock3.denselayer12.norm2.weight, %0.densenet.features.denseblock3.denselayer12.norm2.bias, %0.densenet.features.denseblock3.denselayer12.norm2.running_mean, %0.densenet.features.denseblock3.denselayer12.norm2.running_var)\n",
      "  %999 = Relu(%998)\n",
      "  %1000 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%999, %0.densenet.features.denseblock3.denselayer12.conv2.weight)\n",
      "  %1001 = Concat[axis = 1](%994, %1000)\n",
      "  %1002 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1001, %0.densenet.features.denseblock3.denselayer13.norm1.weight, %0.densenet.features.denseblock3.denselayer13.norm1.bias, %0.densenet.features.denseblock3.denselayer13.norm1.running_mean, %0.densenet.features.denseblock3.denselayer13.norm1.running_var)\n",
      "  %1003 = Relu(%1002)\n",
      "  %1004 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1003, %0.densenet.features.denseblock3.denselayer13.conv1.weight)\n",
      "  %1005 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1004, %0.densenet.features.denseblock3.denselayer13.norm2.weight, %0.densenet.features.denseblock3.denselayer13.norm2.bias, %0.densenet.features.denseblock3.denselayer13.norm2.running_mean, %0.densenet.features.denseblock3.denselayer13.norm2.running_var)\n",
      "  %1006 = Relu(%1005)\n",
      "  %1007 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1006, %0.densenet.features.denseblock3.denselayer13.conv2.weight)\n",
      "  %1008 = Concat[axis = 1](%1001, %1007)\n",
      "  %1009 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1008, %0.densenet.features.denseblock3.denselayer14.norm1.weight, %0.densenet.features.denseblock3.denselayer14.norm1.bias, %0.densenet.features.denseblock3.denselayer14.norm1.running_mean, %0.densenet.features.denseblock3.denselayer14.norm1.running_var)\n",
      "  %1010 = Relu(%1009)\n",
      "  %1011 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1010, %0.densenet.features.denseblock3.denselayer14.conv1.weight)\n",
      "  %1012 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1011, %0.densenet.features.denseblock3.denselayer14.norm2.weight, %0.densenet.features.denseblock3.denselayer14.norm2.bias, %0.densenet.features.denseblock3.denselayer14.norm2.running_mean, %0.densenet.features.denseblock3.denselayer14.norm2.running_var)\n",
      "  %1013 = Relu(%1012)\n",
      "  %1014 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1013, %0.densenet.features.denseblock3.denselayer14.conv2.weight)\n",
      "  %1015 = Concat[axis = 1](%1008, %1014)\n",
      "  %1016 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1015, %0.densenet.features.denseblock3.denselayer15.norm1.weight, %0.densenet.features.denseblock3.denselayer15.norm1.bias, %0.densenet.features.denseblock3.denselayer15.norm1.running_mean, %0.densenet.features.denseblock3.denselayer15.norm1.running_var)\n",
      "  %1017 = Relu(%1016)\n",
      "  %1018 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1017, %0.densenet.features.denseblock3.denselayer15.conv1.weight)\n",
      "  %1019 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1018, %0.densenet.features.denseblock3.denselayer15.norm2.weight, %0.densenet.features.denseblock3.denselayer15.norm2.bias, %0.densenet.features.denseblock3.denselayer15.norm2.running_mean, %0.densenet.features.denseblock3.denselayer15.norm2.running_var)\n",
      "  %1020 = Relu(%1019)\n",
      "  %1021 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1020, %0.densenet.features.denseblock3.denselayer15.conv2.weight)\n",
      "  %1022 = Concat[axis = 1](%1015, %1021)\n",
      "  %1023 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1022, %0.densenet.features.denseblock3.denselayer16.norm1.weight, %0.densenet.features.denseblock3.denselayer16.norm1.bias, %0.densenet.features.denseblock3.denselayer16.norm1.running_mean, %0.densenet.features.denseblock3.denselayer16.norm1.running_var)\n",
      "  %1024 = Relu(%1023)\n",
      "  %1025 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1024, %0.densenet.features.denseblock3.denselayer16.conv1.weight)\n",
      "  %1026 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1025, %0.densenet.features.denseblock3.denselayer16.norm2.weight, %0.densenet.features.denseblock3.denselayer16.norm2.bias, %0.densenet.features.denseblock3.denselayer16.norm2.running_mean, %0.densenet.features.denseblock3.denselayer16.norm2.running_var)\n",
      "  %1027 = Relu(%1026)\n",
      "  %1028 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1027, %0.densenet.features.denseblock3.denselayer16.conv2.weight)\n",
      "  %1029 = Concat[axis = 1](%1022, %1028)\n",
      "  %1030 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1029, %0.densenet.features.denseblock3.denselayer17.norm1.weight, %0.densenet.features.denseblock3.denselayer17.norm1.bias, %0.densenet.features.denseblock3.denselayer17.norm1.running_mean, %0.densenet.features.denseblock3.denselayer17.norm1.running_var)\n",
      "  %1031 = Relu(%1030)\n",
      "  %1032 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1031, %0.densenet.features.denseblock3.denselayer17.conv1.weight)\n",
      "  %1033 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1032, %0.densenet.features.denseblock3.denselayer17.norm2.weight, %0.densenet.features.denseblock3.denselayer17.norm2.bias, %0.densenet.features.denseblock3.denselayer17.norm2.running_mean, %0.densenet.features.denseblock3.denselayer17.norm2.running_var)\n",
      "  %1034 = Relu(%1033)\n",
      "  %1035 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1034, %0.densenet.features.denseblock3.denselayer17.conv2.weight)\n",
      "  %1036 = Concat[axis = 1](%1029, %1035)\n",
      "  %1037 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1036, %0.densenet.features.denseblock3.denselayer18.norm1.weight, %0.densenet.features.denseblock3.denselayer18.norm1.bias, %0.densenet.features.denseblock3.denselayer18.norm1.running_mean, %0.densenet.features.denseblock3.denselayer18.norm1.running_var)\n",
      "  %1038 = Relu(%1037)\n",
      "  %1039 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1038, %0.densenet.features.denseblock3.denselayer18.conv1.weight)\n",
      "  %1040 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1039, %0.densenet.features.denseblock3.denselayer18.norm2.weight, %0.densenet.features.denseblock3.denselayer18.norm2.bias, %0.densenet.features.denseblock3.denselayer18.norm2.running_mean, %0.densenet.features.denseblock3.denselayer18.norm2.running_var)\n",
      "  %1041 = Relu(%1040)\n",
      "  %1042 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1041, %0.densenet.features.denseblock3.denselayer18.conv2.weight)\n",
      "  %1043 = Concat[axis = 1](%1036, %1042)\n",
      "  %1044 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1043, %0.densenet.features.denseblock3.denselayer19.norm1.weight, %0.densenet.features.denseblock3.denselayer19.norm1.bias, %0.densenet.features.denseblock3.denselayer19.norm1.running_mean, %0.densenet.features.denseblock3.denselayer19.norm1.running_var)\n",
      "  %1045 = Relu(%1044)\n",
      "  %1046 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1045, %0.densenet.features.denseblock3.denselayer19.conv1.weight)\n",
      "  %1047 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1046, %0.densenet.features.denseblock3.denselayer19.norm2.weight, %0.densenet.features.denseblock3.denselayer19.norm2.bias, %0.densenet.features.denseblock3.denselayer19.norm2.running_mean, %0.densenet.features.denseblock3.denselayer19.norm2.running_var)\n",
      "  %1048 = Relu(%1047)\n",
      "  %1049 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1048, %0.densenet.features.denseblock3.denselayer19.conv2.weight)\n",
      "  %1050 = Concat[axis = 1](%1043, %1049)\n",
      "  %1051 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1050, %0.densenet.features.denseblock3.denselayer20.norm1.weight, %0.densenet.features.denseblock3.denselayer20.norm1.bias, %0.densenet.features.denseblock3.denselayer20.norm1.running_mean, %0.densenet.features.denseblock3.denselayer20.norm1.running_var)\n",
      "  %1052 = Relu(%1051)\n",
      "  %1053 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1052, %0.densenet.features.denseblock3.denselayer20.conv1.weight)\n",
      "  %1054 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1053, %0.densenet.features.denseblock3.denselayer20.norm2.weight, %0.densenet.features.denseblock3.denselayer20.norm2.bias, %0.densenet.features.denseblock3.denselayer20.norm2.running_mean, %0.densenet.features.denseblock3.denselayer20.norm2.running_var)\n",
      "  %1055 = Relu(%1054)\n",
      "  %1056 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1055, %0.densenet.features.denseblock3.denselayer20.conv2.weight)\n",
      "  %1057 = Concat[axis = 1](%1050, %1056)\n",
      "  %1058 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1057, %0.densenet.features.denseblock3.denselayer21.norm1.weight, %0.densenet.features.denseblock3.denselayer21.norm1.bias, %0.densenet.features.denseblock3.denselayer21.norm1.running_mean, %0.densenet.features.denseblock3.denselayer21.norm1.running_var)\n",
      "  %1059 = Relu(%1058)\n",
      "  %1060 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1059, %0.densenet.features.denseblock3.denselayer21.conv1.weight)\n",
      "  %1061 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1060, %0.densenet.features.denseblock3.denselayer21.norm2.weight, %0.densenet.features.denseblock3.denselayer21.norm2.bias, %0.densenet.features.denseblock3.denselayer21.norm2.running_mean, %0.densenet.features.denseblock3.denselayer21.norm2.running_var)\n",
      "  %1062 = Relu(%1061)\n",
      "  %1063 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1062, %0.densenet.features.denseblock3.denselayer21.conv2.weight)\n",
      "  %1064 = Concat[axis = 1](%1057, %1063)\n",
      "  %1065 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1064, %0.densenet.features.denseblock3.denselayer22.norm1.weight, %0.densenet.features.denseblock3.denselayer22.norm1.bias, %0.densenet.features.denseblock3.denselayer22.norm1.running_mean, %0.densenet.features.denseblock3.denselayer22.norm1.running_var)\n",
      "  %1066 = Relu(%1065)\n",
      "  %1067 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1066, %0.densenet.features.denseblock3.denselayer22.conv1.weight)\n",
      "  %1068 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1067, %0.densenet.features.denseblock3.denselayer22.norm2.weight, %0.densenet.features.denseblock3.denselayer22.norm2.bias, %0.densenet.features.denseblock3.denselayer22.norm2.running_mean, %0.densenet.features.denseblock3.denselayer22.norm2.running_var)\n",
      "  %1069 = Relu(%1068)\n",
      "  %1070 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1069, %0.densenet.features.denseblock3.denselayer22.conv2.weight)\n",
      "  %1071 = Concat[axis = 1](%1064, %1070)\n",
      "  %1072 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1071, %0.densenet.features.denseblock3.denselayer23.norm1.weight, %0.densenet.features.denseblock3.denselayer23.norm1.bias, %0.densenet.features.denseblock3.denselayer23.norm1.running_mean, %0.densenet.features.denseblock3.denselayer23.norm1.running_var)\n",
      "  %1073 = Relu(%1072)\n",
      "  %1074 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1073, %0.densenet.features.denseblock3.denselayer23.conv1.weight)\n",
      "  %1075 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1074, %0.densenet.features.denseblock3.denselayer23.norm2.weight, %0.densenet.features.denseblock3.denselayer23.norm2.bias, %0.densenet.features.denseblock3.denselayer23.norm2.running_mean, %0.densenet.features.denseblock3.denselayer23.norm2.running_var)\n",
      "  %1076 = Relu(%1075)\n",
      "  %1077 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1076, %0.densenet.features.denseblock3.denselayer23.conv2.weight)\n",
      "  %1078 = Concat[axis = 1](%1071, %1077)\n",
      "  %1079 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1078, %0.densenet.features.denseblock3.denselayer24.norm1.weight, %0.densenet.features.denseblock3.denselayer24.norm1.bias, %0.densenet.features.denseblock3.denselayer24.norm1.running_mean, %0.densenet.features.denseblock3.denselayer24.norm1.running_var)\n",
      "  %1080 = Relu(%1079)\n",
      "  %1081 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1080, %0.densenet.features.denseblock3.denselayer24.conv1.weight)\n",
      "  %1082 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1081, %0.densenet.features.denseblock3.denselayer24.norm2.weight, %0.densenet.features.denseblock3.denselayer24.norm2.bias, %0.densenet.features.denseblock3.denselayer24.norm2.running_mean, %0.densenet.features.denseblock3.denselayer24.norm2.running_var)\n",
      "  %1083 = Relu(%1082)\n",
      "  %1084 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1083, %0.densenet.features.denseblock3.denselayer24.conv2.weight)\n",
      "  %1085 = Concat[axis = 1](%1078, %1084)\n",
      "  %1086 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1085, %0.densenet.features.transition3.norm.weight, %0.densenet.features.transition3.norm.bias, %0.densenet.features.transition3.norm.running_mean, %0.densenet.features.transition3.norm.running_var)\n",
      "  %1087 = Relu(%1086)\n",
      "  %1088 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1087, %0.densenet.features.transition3.conv.weight)\n",
      "  %1089 = Pad[mode = 'constant', pads = [0, 0, 0, 0, 0, 0, 0, 0], value = 0](%1088)\n",
      "  %1090 = AveragePool[kernel_shape = [2, 2], pads = [0, 0, 0, 0], strides = [2, 2]](%1089)\n",
      "  %1091 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1090, %0.densenet.features.denseblock4.denselayer1.norm1.weight, %0.densenet.features.denseblock4.denselayer1.norm1.bias, %0.densenet.features.denseblock4.denselayer1.norm1.running_mean, %0.densenet.features.denseblock4.denselayer1.norm1.running_var)\n",
      "  %1092 = Relu(%1091)\n",
      "  %1093 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1092, %0.densenet.features.denseblock4.denselayer1.conv1.weight)\n",
      "  %1094 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1093, %0.densenet.features.denseblock4.denselayer1.norm2.weight, %0.densenet.features.denseblock4.denselayer1.norm2.bias, %0.densenet.features.denseblock4.denselayer1.norm2.running_mean, %0.densenet.features.denseblock4.denselayer1.norm2.running_var)\n",
      "  %1095 = Relu(%1094)\n",
      "  %1096 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1095, %0.densenet.features.denseblock4.denselayer1.conv2.weight)\n",
      "  %1097 = Concat[axis = 1](%1090, %1096)\n",
      "  %1098 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1097, %0.densenet.features.denseblock4.denselayer2.norm1.weight, %0.densenet.features.denseblock4.denselayer2.norm1.bias, %0.densenet.features.denseblock4.denselayer2.norm1.running_mean, %0.densenet.features.denseblock4.denselayer2.norm1.running_var)\n",
      "  %1099 = Relu(%1098)\n",
      "  %1100 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1099, %0.densenet.features.denseblock4.denselayer2.conv1.weight)\n",
      "  %1101 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1100, %0.densenet.features.denseblock4.denselayer2.norm2.weight, %0.densenet.features.denseblock4.denselayer2.norm2.bias, %0.densenet.features.denseblock4.denselayer2.norm2.running_mean, %0.densenet.features.denseblock4.denselayer2.norm2.running_var)\n",
      "  %1102 = Relu(%1101)\n",
      "  %1103 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1102, %0.densenet.features.denseblock4.denselayer2.conv2.weight)\n",
      "  %1104 = Concat[axis = 1](%1097, %1103)\n",
      "  %1105 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1104, %0.densenet.features.denseblock4.denselayer3.norm1.weight, %0.densenet.features.denseblock4.denselayer3.norm1.bias, %0.densenet.features.denseblock4.denselayer3.norm1.running_mean, %0.densenet.features.denseblock4.denselayer3.norm1.running_var)\n",
      "  %1106 = Relu(%1105)\n",
      "  %1107 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1106, %0.densenet.features.denseblock4.denselayer3.conv1.weight)\n",
      "  %1108 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1107, %0.densenet.features.denseblock4.denselayer3.norm2.weight, %0.densenet.features.denseblock4.denselayer3.norm2.bias, %0.densenet.features.denseblock4.denselayer3.norm2.running_mean, %0.densenet.features.denseblock4.denselayer3.norm2.running_var)\n",
      "  %1109 = Relu(%1108)\n",
      "  %1110 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1109, %0.densenet.features.denseblock4.denselayer3.conv2.weight)\n",
      "  %1111 = Concat[axis = 1](%1104, %1110)\n",
      "  %1112 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1111, %0.densenet.features.denseblock4.denselayer4.norm1.weight, %0.densenet.features.denseblock4.denselayer4.norm1.bias, %0.densenet.features.denseblock4.denselayer4.norm1.running_mean, %0.densenet.features.denseblock4.denselayer4.norm1.running_var)\n",
      "  %1113 = Relu(%1112)\n",
      "  %1114 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1113, %0.densenet.features.denseblock4.denselayer4.conv1.weight)\n",
      "  %1115 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1114, %0.densenet.features.denseblock4.denselayer4.norm2.weight, %0.densenet.features.denseblock4.denselayer4.norm2.bias, %0.densenet.features.denseblock4.denselayer4.norm2.running_mean, %0.densenet.features.denseblock4.denselayer4.norm2.running_var)\n",
      "  %1116 = Relu(%1115)\n",
      "  %1117 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1116, %0.densenet.features.denseblock4.denselayer4.conv2.weight)\n",
      "  %1118 = Concat[axis = 1](%1111, %1117)\n",
      "  %1119 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1118, %0.densenet.features.denseblock4.denselayer5.norm1.weight, %0.densenet.features.denseblock4.denselayer5.norm1.bias, %0.densenet.features.denseblock4.denselayer5.norm1.running_mean, %0.densenet.features.denseblock4.denselayer5.norm1.running_var)\n",
      "  %1120 = Relu(%1119)\n",
      "  %1121 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1120, %0.densenet.features.denseblock4.denselayer5.conv1.weight)\n",
      "  %1122 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1121, %0.densenet.features.denseblock4.denselayer5.norm2.weight, %0.densenet.features.denseblock4.denselayer5.norm2.bias, %0.densenet.features.denseblock4.denselayer5.norm2.running_mean, %0.densenet.features.denseblock4.denselayer5.norm2.running_var)\n",
      "  %1123 = Relu(%1122)\n",
      "  %1124 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1123, %0.densenet.features.denseblock4.denselayer5.conv2.weight)\n",
      "  %1125 = Concat[axis = 1](%1118, %1124)\n",
      "  %1126 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1125, %0.densenet.features.denseblock4.denselayer6.norm1.weight, %0.densenet.features.denseblock4.denselayer6.norm1.bias, %0.densenet.features.denseblock4.denselayer6.norm1.running_mean, %0.densenet.features.denseblock4.denselayer6.norm1.running_var)\n",
      "  %1127 = Relu(%1126)\n",
      "  %1128 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1127, %0.densenet.features.denseblock4.denselayer6.conv1.weight)\n",
      "  %1129 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1128, %0.densenet.features.denseblock4.denselayer6.norm2.weight, %0.densenet.features.denseblock4.denselayer6.norm2.bias, %0.densenet.features.denseblock4.denselayer6.norm2.running_mean, %0.densenet.features.denseblock4.denselayer6.norm2.running_var)\n",
      "  %1130 = Relu(%1129)\n",
      "  %1131 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1130, %0.densenet.features.denseblock4.denselayer6.conv2.weight)\n",
      "  %1132 = Concat[axis = 1](%1125, %1131)\n",
      "  %1133 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1132, %0.densenet.features.denseblock4.denselayer7.norm1.weight, %0.densenet.features.denseblock4.denselayer7.norm1.bias, %0.densenet.features.denseblock4.denselayer7.norm1.running_mean, %0.densenet.features.denseblock4.denselayer7.norm1.running_var)\n",
      "  %1134 = Relu(%1133)\n",
      "  %1135 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1134, %0.densenet.features.denseblock4.denselayer7.conv1.weight)\n",
      "  %1136 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1135, %0.densenet.features.denseblock4.denselayer7.norm2.weight, %0.densenet.features.denseblock4.denselayer7.norm2.bias, %0.densenet.features.denseblock4.denselayer7.norm2.running_mean, %0.densenet.features.denseblock4.denselayer7.norm2.running_var)\n",
      "  %1137 = Relu(%1136)\n",
      "  %1138 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1137, %0.densenet.features.denseblock4.denselayer7.conv2.weight)\n",
      "  %1139 = Concat[axis = 1](%1132, %1138)\n",
      "  %1140 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1139, %0.densenet.features.denseblock4.denselayer8.norm1.weight, %0.densenet.features.denseblock4.denselayer8.norm1.bias, %0.densenet.features.denseblock4.denselayer8.norm1.running_mean, %0.densenet.features.denseblock4.denselayer8.norm1.running_var)\n",
      "  %1141 = Relu(%1140)\n",
      "  %1142 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1141, %0.densenet.features.denseblock4.denselayer8.conv1.weight)\n",
      "  %1143 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1142, %0.densenet.features.denseblock4.denselayer8.norm2.weight, %0.densenet.features.denseblock4.denselayer8.norm2.bias, %0.densenet.features.denseblock4.denselayer8.norm2.running_mean, %0.densenet.features.denseblock4.denselayer8.norm2.running_var)\n",
      "  %1144 = Relu(%1143)\n",
      "  %1145 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1144, %0.densenet.features.denseblock4.denselayer8.conv2.weight)\n",
      "  %1146 = Concat[axis = 1](%1139, %1145)\n",
      "  %1147 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1146, %0.densenet.features.denseblock4.denselayer9.norm1.weight, %0.densenet.features.denseblock4.denselayer9.norm1.bias, %0.densenet.features.denseblock4.denselayer9.norm1.running_mean, %0.densenet.features.denseblock4.denselayer9.norm1.running_var)\n",
      "  %1148 = Relu(%1147)\n",
      "  %1149 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1148, %0.densenet.features.denseblock4.denselayer9.conv1.weight)\n",
      "  %1150 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1149, %0.densenet.features.denseblock4.denselayer9.norm2.weight, %0.densenet.features.denseblock4.denselayer9.norm2.bias, %0.densenet.features.denseblock4.denselayer9.norm2.running_mean, %0.densenet.features.denseblock4.denselayer9.norm2.running_var)\n",
      "  %1151 = Relu(%1150)\n",
      "  %1152 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1151, %0.densenet.features.denseblock4.denselayer9.conv2.weight)\n",
      "  %1153 = Concat[axis = 1](%1146, %1152)\n",
      "  %1154 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1153, %0.densenet.features.denseblock4.denselayer10.norm1.weight, %0.densenet.features.denseblock4.denselayer10.norm1.bias, %0.densenet.features.denseblock4.denselayer10.norm1.running_mean, %0.densenet.features.denseblock4.denselayer10.norm1.running_var)\n",
      "  %1155 = Relu(%1154)\n",
      "  %1156 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1155, %0.densenet.features.denseblock4.denselayer10.conv1.weight)\n",
      "  %1157 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1156, %0.densenet.features.denseblock4.denselayer10.norm2.weight, %0.densenet.features.denseblock4.denselayer10.norm2.bias, %0.densenet.features.denseblock4.denselayer10.norm2.running_mean, %0.densenet.features.denseblock4.denselayer10.norm2.running_var)\n",
      "  %1158 = Relu(%1157)\n",
      "  %1159 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1158, %0.densenet.features.denseblock4.denselayer10.conv2.weight)\n",
      "  %1160 = Concat[axis = 1](%1153, %1159)\n",
      "  %1161 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1160, %0.densenet.features.denseblock4.denselayer11.norm1.weight, %0.densenet.features.denseblock4.denselayer11.norm1.bias, %0.densenet.features.denseblock4.denselayer11.norm1.running_mean, %0.densenet.features.denseblock4.denselayer11.norm1.running_var)\n",
      "  %1162 = Relu(%1161)\n",
      "  %1163 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1162, %0.densenet.features.denseblock4.denselayer11.conv1.weight)\n",
      "  %1164 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1163, %0.densenet.features.denseblock4.denselayer11.norm2.weight, %0.densenet.features.denseblock4.denselayer11.norm2.bias, %0.densenet.features.denseblock4.denselayer11.norm2.running_mean, %0.densenet.features.denseblock4.denselayer11.norm2.running_var)\n",
      "  %1165 = Relu(%1164)\n",
      "  %1166 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1165, %0.densenet.features.denseblock4.denselayer11.conv2.weight)\n",
      "  %1167 = Concat[axis = 1](%1160, %1166)\n",
      "  %1168 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1167, %0.densenet.features.denseblock4.denselayer12.norm1.weight, %0.densenet.features.denseblock4.denselayer12.norm1.bias, %0.densenet.features.denseblock4.denselayer12.norm1.running_mean, %0.densenet.features.denseblock4.denselayer12.norm1.running_var)\n",
      "  %1169 = Relu(%1168)\n",
      "  %1170 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1169, %0.densenet.features.denseblock4.denselayer12.conv1.weight)\n",
      "  %1171 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1170, %0.densenet.features.denseblock4.denselayer12.norm2.weight, %0.densenet.features.denseblock4.denselayer12.norm2.bias, %0.densenet.features.denseblock4.denselayer12.norm2.running_mean, %0.densenet.features.denseblock4.denselayer12.norm2.running_var)\n",
      "  %1172 = Relu(%1171)\n",
      "  %1173 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1172, %0.densenet.features.denseblock4.denselayer12.conv2.weight)\n",
      "  %1174 = Concat[axis = 1](%1167, %1173)\n",
      "  %1175 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1174, %0.densenet.features.denseblock4.denselayer13.norm1.weight, %0.densenet.features.denseblock4.denselayer13.norm1.bias, %0.densenet.features.denseblock4.denselayer13.norm1.running_mean, %0.densenet.features.denseblock4.denselayer13.norm1.running_var)\n",
      "  %1176 = Relu(%1175)\n",
      "  %1177 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1176, %0.densenet.features.denseblock4.denselayer13.conv1.weight)\n",
      "  %1178 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1177, %0.densenet.features.denseblock4.denselayer13.norm2.weight, %0.densenet.features.denseblock4.denselayer13.norm2.bias, %0.densenet.features.denseblock4.denselayer13.norm2.running_mean, %0.densenet.features.denseblock4.denselayer13.norm2.running_var)\n",
      "  %1179 = Relu(%1178)\n",
      "  %1180 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1179, %0.densenet.features.denseblock4.denselayer13.conv2.weight)\n",
      "  %1181 = Concat[axis = 1](%1174, %1180)\n",
      "  %1182 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1181, %0.densenet.features.denseblock4.denselayer14.norm1.weight, %0.densenet.features.denseblock4.denselayer14.norm1.bias, %0.densenet.features.denseblock4.denselayer14.norm1.running_mean, %0.densenet.features.denseblock4.denselayer14.norm1.running_var)\n",
      "  %1183 = Relu(%1182)\n",
      "  %1184 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1183, %0.densenet.features.denseblock4.denselayer14.conv1.weight)\n",
      "  %1185 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1184, %0.densenet.features.denseblock4.denselayer14.norm2.weight, %0.densenet.features.denseblock4.denselayer14.norm2.bias, %0.densenet.features.denseblock4.denselayer14.norm2.running_mean, %0.densenet.features.denseblock4.denselayer14.norm2.running_var)\n",
      "  %1186 = Relu(%1185)\n",
      "  %1187 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1186, %0.densenet.features.denseblock4.denselayer14.conv2.weight)\n",
      "  %1188 = Concat[axis = 1](%1181, %1187)\n",
      "  %1189 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1188, %0.densenet.features.denseblock4.denselayer15.norm1.weight, %0.densenet.features.denseblock4.denselayer15.norm1.bias, %0.densenet.features.denseblock4.denselayer15.norm1.running_mean, %0.densenet.features.denseblock4.denselayer15.norm1.running_var)\n",
      "  %1190 = Relu(%1189)\n",
      "  %1191 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1190, %0.densenet.features.denseblock4.denselayer15.conv1.weight)\n",
      "  %1192 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1191, %0.densenet.features.denseblock4.denselayer15.norm2.weight, %0.densenet.features.denseblock4.denselayer15.norm2.bias, %0.densenet.features.denseblock4.denselayer15.norm2.running_mean, %0.densenet.features.denseblock4.denselayer15.norm2.running_var)\n",
      "  %1193 = Relu(%1192)\n",
      "  %1194 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1193, %0.densenet.features.denseblock4.denselayer15.conv2.weight)\n",
      "  %1195 = Concat[axis = 1](%1188, %1194)\n",
      "  %1196 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1195, %0.densenet.features.denseblock4.denselayer16.norm1.weight, %0.densenet.features.denseblock4.denselayer16.norm1.bias, %0.densenet.features.denseblock4.denselayer16.norm1.running_mean, %0.densenet.features.denseblock4.denselayer16.norm1.running_var)\n",
      "  %1197 = Relu(%1196)\n",
      "  %1198 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1197, %0.densenet.features.denseblock4.denselayer16.conv1.weight)\n",
      "  %1199 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1198, %0.densenet.features.denseblock4.denselayer16.norm2.weight, %0.densenet.features.denseblock4.denselayer16.norm2.bias, %0.densenet.features.denseblock4.denselayer16.norm2.running_mean, %0.densenet.features.denseblock4.denselayer16.norm2.running_var)\n",
      "  %1200 = Relu(%1199)\n",
      "  %1201 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1200, %0.densenet.features.denseblock4.denselayer16.conv2.weight)\n",
      "  %1202 = Concat[axis = 1](%1195, %1201)\n",
      "  %1203 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1202, %0.densenet.features.norm5.weight, %0.densenet.features.norm5.bias, %0.densenet.features.norm5.running_mean, %0.densenet.features.norm5.running_var)\n",
      "  %1204 = ConvTranspose[dilations = [1, 1], group = 1, kernel_shape = [4, 4], pads = [1, 1, 1, 1], strides = [2, 2]](%1203, %1.cmap_up.0.weight, %1.cmap_up.0.bias)\n",
      "  %1205 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1204, %1.cmap_up.1.weight, %1.cmap_up.1.bias, %1.cmap_up.1.running_mean, %1.cmap_up.1.running_var)\n",
      "  %1206 = Relu(%1205)\n",
      "  %1207 = ConvTranspose[dilations = [1, 1], group = 1, kernel_shape = [4, 4], pads = [1, 1, 1, 1], strides = [2, 2]](%1206, %1.cmap_up.3.weight, %1.cmap_up.3.bias)\n",
      "  %1208 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1207, %1.cmap_up.4.weight, %1.cmap_up.4.bias, %1.cmap_up.4.running_mean, %1.cmap_up.4.running_var)\n",
      "  %1209 = Relu(%1208)\n",
      "  %1210 = ConvTranspose[dilations = [1, 1], group = 1, kernel_shape = [4, 4], pads = [1, 1, 1, 1], strides = [2, 2]](%1209, %1.cmap_up.6.weight, %1.cmap_up.6.bias)\n",
      "  %1211 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1210, %1.cmap_up.7.weight, %1.cmap_up.7.bias, %1.cmap_up.7.running_mean, %1.cmap_up.7.running_var)\n",
      "  %1212 = Relu(%1211)\n",
      "  %1213 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1212, %1.cmap_att.weight, %1.cmap_att.bias)\n",
      "  %1214 = Sigmoid(%1213)\n",
      "  %1215 = ConvTranspose[dilations = [1, 1], group = 1, kernel_shape = [4, 4], pads = [1, 1, 1, 1], strides = [2, 2]](%1203, %1.paf_up.0.weight, %1.paf_up.0.bias)\n",
      "  %1216 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1215, %1.paf_up.1.weight, %1.paf_up.1.bias, %1.paf_up.1.running_mean, %1.paf_up.1.running_var)\n",
      "  %1217 = Relu(%1216)\n",
      "  %1218 = ConvTranspose[dilations = [1, 1], group = 1, kernel_shape = [4, 4], pads = [1, 1, 1, 1], strides = [2, 2]](%1217, %1.paf_up.3.weight, %1.paf_up.3.bias)\n",
      "  %1219 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1218, %1.paf_up.4.weight, %1.paf_up.4.bias, %1.paf_up.4.running_mean, %1.paf_up.4.running_var)\n",
      "  %1220 = Relu(%1219)\n",
      "  %1221 = ConvTranspose[dilations = [1, 1], group = 1, kernel_shape = [4, 4], pads = [1, 1, 1, 1], strides = [2, 2]](%1220, %1.paf_up.6.weight, %1.paf_up.6.bias)\n",
      "  %1222 = BatchNormalization[epsilon = 9.99999974737875e-06, momentum = 0.899999976158142](%1221, %1.paf_up.7.weight, %1.paf_up.7.bias, %1.paf_up.7.running_mean, %1.paf_up.7.running_var)\n",
      "  %1223 = Relu(%1222)\n",
      "  %1224 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%1223, %1.paf_att.weight, %1.paf_att.bias)\n",
      "  %1225 = Tanh(%1224)\n",
      "  %1226 = Mul(%1212, %1214)\n",
      "  %1227 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1226, %1.cmap_conv.weight, %1.cmap_conv.bias)\n",
      "  %1228 = Mul(%1223, %1225)\n",
      "  %1229 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%1228, %1.paf_conv.weight, %1.paf_conv.bias)\n",
      "  return %1227, %1229\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import onnx\n",
    "\n",
    "onnx_model = onnx.load(ONNX_OUTPUT)\n",
    "onnx.checker.check_model(onnx_model)\n",
    "\n",
    "# Print a human readable representation of the graph\n",
    "print(onnx.helper.printable_graph(onnx_model.graph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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